Biochemical phenotyping unravels novel metabolic abnormalities and potential biomarkers associated with treatment of GLUT1 deficiency with ketogenic diet

The diagnostic and prognostic role of cerebrospinal fluid biomarkers in glucose transporter 1 deficiency: a systematic review

The purpose of this study is to investigate the diagnostic and prognostic role of cerebrospinal fluid (CSF) biomarkers in the diagnostic work-up of glucose transporter 1 (GLUT1) deficiency. Reported here is a systematic review according to PRISMA guidelines collecting clinical and biochemical data about all published patients who underwent CSF analysis. Clinical phenotypes were compared between groups defined by the levels of CSF glucose (≤ 2.2 mmol/L versus > 2.2 mmol/L), CSF/blood glucose ratio (≤ 0.45 versus > 0.45), and CSF lactate (≤ 1 mmol/L versus > 1 mmol/L). Five hundred sixty-two patients fulfilled the inclusion criteria with a mean age at the diagnosis of 8.6 ± 6.7 years. Patients with CSF glucose ≤ 2.2 mmol/L and CSF/blood glucose ratio ≤ 0.45 presented with an earlier onset of symptoms (16.4 ± 22.0 versus 54.4 ± 45.9 months, p < 0.01; 15.7 ± 23.8 versus 40.9 ± 38.0 months, p < 0.01) and received an earlier molecular genetic confirmation (92.1 ± 72.8 versus 157.1 ± 106.2 months, p < 0.01). CSF glucose ≤ 2.2 mmol/L was consistently associated with response to ketogenic diet (p = 0.018) and antiseizure medications (p = 0.025). CSF/blood glucose ratio ≤ 0.45 was significantly associated with absence seizures (p = 0.048), paroxysmal exercise-induced dyskinesia (p = 0.046), and intellectual disability (p = 0.016) while CSF lactate > 1 mmol/L was associated with a response to antiseizure medications (p = 0.026) but not to ketogenic diet.Conclusions:This systematic review supported the diagnostic usefulness of lumbar puncture for the early identification of patients with GLUT1 deficiency responsive to treatments especially if they present with co-occurring epilepsy, movement, and neurodevelopmental disorders. What is Known: • Phenotypes of GLUT1 deficiency syndrome range between early epileptic and developmental encephalopathy to paroxysmal movement disorders and developmental impairment What is New: • CSF blood/glucose ratio may predict better than CSF glucose the diagnosis in children presenting with early onset absences • CSF blood/glucose ratio may predict better than CSF glucose the diagnosis in children presenting with paroxysmal exercise induced dyskinesia and intellectual disability. • CSF glucose may predict better than CSF blood/glucose and lactate the response to ketogenic diet and antiseizure medications.

The importance of synthetic pharmacotherapy for recessive cerebellar ataxias

INTRODUCTION

The last decade has witnessed major breakthroughs in identifying novel genetic causes of hereditary ataxias, deepening our understanding of disease mechanisms, and developing therapies for these debilitating disorders.

AREAS COVERED

This article reviews the currently approved and most promising candidate pharmacotherapies in relation to the known disease mechanisms of the most prevalent autosomal recessive ataxias. Omaveloxolone is an Nrf2 activator that increases antioxidant defense and was recently approved for treatment of Friedreich ataxia. Its therapeutic effect is modest, and further research is needed to find synergistic treatments that would halt or reverse disease progression. Promising approaches include upregulation of frataxin expression by epigenetic mechanisms, direct protein replacement, and gene replacement therapy. For ataxia-telangiectasia, promising approaches include splice-switching antisense oligonucleotides and small molecules targeting oxidative stress, inflammation, and mitochondrial function. Rare recessive ataxias for which disease-modifying therapies exist are also reviewed, emphasizing recently approved therapies. Evidence supporting the use of riluzole and acetyl-leucine in recessive ataxias is discussed.

EXPERT OPINION

Advances in genetic therapies for other neurogenetic conditions have paved the way to implement feasible approaches with potential dramatic benefits. Particularly, as we develop effective treatments for these conditions, we may need to combine therapies, consider newborn testing for pre-symptomatic treatment, and optimize non-pharmacological approaches.

GLUT-1DS resistant to ketogenic diet: from clinical feature to in silico analysis. An exemplificative case report with a literature review

Glucose transporter type 1 deficiency syndrome (GLUT-1DS) is characterized by alterations in glucose translocation through the blood-brain barrier (BBB) due to mutation involving the GLUT-1 transporter. The fundamental therapy is ketogenic diet (KD) that provide an alternative energetic substrate - ketone bodies that across the BBB via MCT-1 - for the brain. Symptoms are various and include intractable seizure, acquired microcephalia, abnormal ocular movement, movement disorder, and neurodevelopment delay secondary to an energetic crisis for persistent neuroglycopenia. KD is extremely effective in controlling epileptic seizures and has a positive impact on movement disorders and cognitive impairment. Cases of KD resistance are rare, and only a few of them are reported in the literature, all regarding seizure. Our study describes a peculiar case of GLUT-1DS due to a new deletion involving the first codon of SLC2A1 gene determining a loss of function with a resistance to KD admitted to hospital due to intractable episodes of dystonia. This patient presented a worsening of symptomatology at higher ketonemia values but without hyperketosis and showed a complete resolution of symptomatology while maintaining low ketonemia values. Our study proposes an in-silico genomic and proteomic analysis aimed at explaining the atypical response to KD exhibited by our patient. In this way, we propose a new clinical and research approach based on precision medicine and molecular modelling to be applied to patients with GLUT-1DS resistant to first-line treatment with ketogenic diet by in silico study of genetic and altered protein product.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

The Possible Effect of Dietary Fiber Intake on the Metabolic Patterns of Dyslipidemia Subjects: Cross-Sectional Research Using Nontargeted Metabolomics

BACKGROUND

Dyslipidemia is important because of its association with various metabolic complications. Numerous studies have sought to obtain scientific evidence for managing dyslipidemia patients.

OBJECTIVES

This study aims to identify differences in the nutritional traits of dyslipidemia subjects based on metabolite patterns.

METHODS

Dyslipidemia (n = 73) and control (n = 80) subjects were included. Dyslipidemia was defined as triglycerides ≥200 mg/dL, total cholesterol ≥240 mg/dL, low density lipoprotein cholesterol ≥160 mg/dL, high-density lipoprotein cholesterol <40 mg/dL (men) or 50 mg/dL (women), or lipid-lowering medicine use. Nontargeted metabolomics based on ultra-high performance liquid chromatography-mass spectrometry identified plasma metabolites, and K-means clustering was used to reconstitute groups based on the similarity of metabolomic patterns across all subjects. Then, with eXtreme Gradient Boosting, metabolites significantly contributing to the new grouping were selected. Statistical analysis was conducted to analyze traits demonstrating appreciable differences between the groups.

RESULTS

Dyslipidemia subjects were divided into 2 groups based on whether they were (n = 24) or were not (n = 56) in a similar metabolic state as the controls by K-means clustering. The considerable contribution of 4 metabolites (3-hydroxybutyrylcarnitine, 2-octenal, 1,3,5-heptatriene, and 5β-cholanic acid) to this new subset of dyslipidemia was confirmed by eXtreme Gradient Boosting. Furthermore, fiber intake was significantly higher in dyslipidemia subjects whose metabolic state was similar to that of the control than in the dissimilar group (P = 0.002). Moreover, significant correlations were observed between the 4 metabolites and fiber intake. Regression analysis determined that the ideal cutoff for fiber intake was 17.28 g/d.

CONCLUSIONS

Dyslipidemia patients who consume 17.28 g/d or more of dietary fiber may maintain similar metabolic patterns to healthy individuals, with substantial effects on the changes in the concentrations of 4 metabolites. Our findings could be applied to developing dietary guidelines for dyslipidemia patients.

Uncommon use of intermittent glucose administration for infrequent non-epileptic paroxysmal events in GLUT1-DS

The ketogenic diet is the treatment of GLUT1 deficiency syndrome that provides an alternative energy source for the brain. However, there are some limitations, including compliance issues as well as patients who do not respond to the ketogenic diet. We report the case of two patients that were not on any particular diet. Both experienced infrequent paroxysmal non-epileptic events (acute ataxia and exercise-induced dystonia). Intermittent glucose intake prior to physical activity for exercise-induced symptoms and at the onset of symptoms for acute ataxia showed consistent and reproducible improvement of the symptoms. Our observations raised the question of developing a new treatment strategy with the induction of a sustained increase in blood glucose. For now, the use of this strategy should be limited to a small group of GLUT1-DS patients who are not on a ketogenic diet.

Novel cerebrospinal fluid biomarkers of glucose transporter type 1 deficiency syndrome: Implications beyond the brain's energy deficit

We used next-generation metabolic screening to identify new biomarkers for improved diagnosis and pathophysiological understanding of glucose transporter type 1 deficiency syndrome (GLUT1DS), comparing metabolic cerebrospinal fluid (CSF) profiles from 12 patients to those of 116 controls. This confirmed decreased CSF glucose and lactate levels in patients with GLUT1DS and increased glutamine at group level. We identified three novel biomarkers significantly decreased in patients, namely gluconic + galactonic acid, xylose-α1-3-glucose, and xylose-α1-3-xylose-α1-3-glucose, of which the latter two have not previously been identified in body fluids. CSF concentrations of gluconic + galactonic acid may be reduced as these metabolites could serve as alternative substrates for the pentose phosphate pathway. Xylose-α1-3-glucose and xylose-α1-3-xylose-α1-3-glucose may originate from glycosylated proteins; their decreased levels are hypothetically the consequence of insufficient glucose, one of two substrates for O-glucosylation. Since many proteins are O-glucosylated, this deficiency may affect cellular processes and thus contribute to GLUT1DS pathophysiology. The novel CSF biomarkers have the potential to improve the biochemical diagnosis of GLUT1DS. Our findings imply that brain glucose deficiency in GLUT1DS may cause disruptions at the cellular level that go beyond energy metabolism, underlining the importance of developing treatment strategies that directly target cerebral glucose uptake.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy

The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

Understanding Inborn Errors of Metabolism through Metabolomics

Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism

IMPORTANCE

Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing.

OBJECTIVE

To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020.

EXPOSURES

Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate.

MAIN OUTCOMES AND MEASURES

The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS.

RESULTS

Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS.

CONCLUSIONS AND RELEVANCE

These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism

BACKGROUND

The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways.

METHODS

We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms.

RESULTS

The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches.

CONCLUSIONS

This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.

Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives

Introduction

Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood).

Methods

Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests.

Results

We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types.

Conclusions

Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies.

Impact statement

In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers.

Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics

INTRODUCTION

Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing.

OBJECTIVES

To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis.

METHODS

Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition.

RESULTS

We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles.

CONCLUSIONS

Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.

Classic Ketogenic Diet and Modified Atkins Diet in SLC2A1 Positive and Negative Patients with Suspected GLUT1 Deficiency Syndrome: A Single Center Analysis of 18 Cases

Background: Glucose transporter type 1 deficiency syndrome (GLUT1DS) is caused by mutations in the SLC2A1 gene and produces seizures, neurodevelopmental impairment, and movement disorders. Ketogenic dietary therapies (KDT) are the gold standard treatment. Similar symptoms may appear in SLC2A1 negative patients. The purpose is to evaluate the effectiveness of KDT in children with GLUT1DS suspected SLC2A1 (+) and (-), side effects (SE), and the impact on patients nutritional status. Methods: An observational descriptive study was conducted to describe 18 children (January 2009–August 2020). SLC2A1 analysis, seizures, movement disorder, anti-epileptic drugs (AEDS), anthropometry, SE, and laboratory assessment were monitored baseline and at 3, 6, 12, and 24 months after the onset of KDT. Results: 6/18 were SLC2A1(+) and 13/18 had seizures. In these groups, the age for debut of symptoms was higher. The mean time from debut to KDT onset was higher in SLC2A1(+). The modified Atkins diet (MAD) was used in 12 (5 SLC2A1(+)). Movement disorder improved (4/5), and a reduction in seizures >50% compared to baseline was achieved in more than half of the epileptic children throughout the follow-up. No differences in effectiveness were found according to the type of KDT. Early SE occurred in 33%. Long-term SE occurred in 10, 5, 7, and 5 children throughout the follow-up. The most frequent SE were constipation, hypercalciuria, and hyperlipidaemia. No differences in growth were found according to the SLC2A1 mutation or type of KDT. Conclusions: CKD and MAD were effective for SLC2A1 positive and negative patients in our cohort. SE were frequent, but mild. Permanent monitoring should be made to identify SE and nutritional deficits.

Differential ketogenic diet-induced shift in CSF lipid/carbohydrate metabolome of pediatric epilepsy patients with optimal vs. no anticonvulsant response: a pilot study

Background

The low carbohydrate, high fat ketogenic diet can be an effective anticonvulsant treatment in some pediatric patients with pharmacoresistant epilepsy. Its mechanism(s) of action, however, remain uncertain. Direct sampling of cerebrospinal fluid before and during metabolic therapy may reveal key changes associated with differential clinical outcomes. We characterized the relationship between seizure responsiveness and changes in lipid and carbohydrate metabolites.

Methods

We performed metabolomic analysis of cerebrospinal fluid samples taken before and during ketogenic diet treatment in patients with optimal response (100% seizure remission) and patients with no response (no seizure improvement) to search for differential diet effects in hallmark metabolic compounds in these two groups. Optimal responders and non-responders were similar in age range and included males and females. Seizure types and the etiologies or syndromes of epilepsy varied but did not appear to differ systematically between responders and non-responders.

Results

Analysis showed a strong effect of ketogenic diet treatment on the cerebrospinal fluid metabolome. Longitudinal and between-subjects analyses revealed that many lipids and carbohydrates were changed significantly by ketogenic diet, with changes typically being of larger magnitude in responders. Notably, responders had more robust changes in glucose and the ketone bodies β-hydroxybutyrate and acetoacetate than non-responders; conversely, non-responders had significant increases in fructose and sorbose, which did not occur in responders.

Conclusions

The data suggest that a differential and stronger metabolic response to the ketogenic diet may predict a better anticonvulsant response, and such variability is likely due to inherent biological factors of individual patients. Strategies to boost the metabolic response may be beneficial.

Variety of symptoms of GLUT1 deficiency syndrome in three-generation family

OBJECTIVE

Glucose transporter type 1 deficiency (G1D) syndrome is generally a genetic disorder because of a mutation of the SLC2A1 gene. The clinical picture of G1D is heterogeneous. The aim of this paper was to present the case of G1D, recognized in a three-generation family, caused by missense mutation p.Arg92Trp in SLC2A1 gene, and showing high clinical heterogeneity and evolution of symptoms over time.

METHODS

Three-generation family members, showing symptoms suggesting G1D, have been characterized in terms of the clinical picture, electroencephalogram (EEG) recordings, brain neuroimaging, and the psychological assessment data. All subjects were offered genetic testing of the SLC2A1 gene.

RESULTS

We sequenced the SLC2A1 gene in the proband of the family and identified the c.274C > T variant (p.Arg92Trp). The presence of the same mutation was confirmed in all affected family members; however, significant variations in the clinical picture among them were observed. In addition to the typical symptoms for G1D (e.g., epilepsy, intellectual disability), patients presented movement disorders, stiffness, and dysarthria, as well as psychiatric symptoms. After using the ketogenic diet, epileptic seizures disappeared, but the rest of the symptoms were resistant to treatment.

CONCLUSIONS

Despite the same underlying mutation, clinical symptoms may vary among members of one family. Different clinical symptoms are observed depending on the patient's age. Not all symptoms occur in all patients within one family despite the same genetic background. However, the importance of early therapy for the clinical course of the disease requires further study.

Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

Early diagnosis of inborn errors of metabolism (IEM)—a large group of congenital disorders—is critical, given that many respond well to targeted therapy. Newborn screening programs successfully capture a proportion of patients enabling early recognition and prompt initiation of therapy. For others, the heterogeneity in clinical presentation often confuses diagnosis with more common conditions. In the absence of family history and following clinical suspicion, the laboratory diagnosis typically begins with broad screening tests to circumscribe specialised metabolite and/or enzyme assays to identify the specific IEM. Confirmation of the biochemical diagnosis is usually achieved by identifying pathogenic genetic variants that will also enable cascade testing for family members. Unsurprisingly, this diagnostic trajectory is too often a protracted and lengthy process resulting in delays in diagnosis and, importantly, therapeutic intervention for these rare conditions is also postponed. Implementation of mass spectrometry technologies coupled with the expanding field of metabolomics is changing the landscape of diagnosing IEM as numerous metabolites, as well as enzymes, can now be measured collectively on a single mass spectrometry-based platform. As the biochemical consequences of impaired metabolism continue to be elucidated, the measurement of secondary metabolites common across groups of IEM will facilitate algorithms to further increase the efficiency of diagnosis.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Sphingolipid Metabolism Perturbations in Rett Syndrome

Rett syndrome is a severe neurodevelopmental disorder affecting mostly females and is caused by loss-of-function mutations in the MECP2 gene that encoded the methyl-CpG-binding protein 2. The pathogenetic mechanisms of Rett syndrome are not completely understood and metabolic derangements are emerging as features of Rett syndrome. We performed a semi-quantitative tandem mass spectrometry-based analysis that measured over 900 metabolites on blood samples from 14 female subjects with Rett syndrome carrying MECP2 mutations. The metabolic profiling revealed alterations in lipids, mostly involved in sphingolipid metabolism, and sphinganine/sphingosine, that are known to have a neurotrophic role. Further investigations are required to understand the mechanisms underlying such perturbations and their significance in the disease pathogenesis. Nevertheless, these metabolites are attractive for studies on the disease pathogenesis and as potential disease biomarkers.

New insights into GluT1 mechanics during glucose transfer

Glucose plays a crucial role in the mammalian cell metabolism. In the erythrocytes and endothelial cells of the blood-brain barrier, glucose uptake is mediated by the glucose transporter type 1 (GluT1). GluT1 deficiency or mutations cause severe physiological disorders. GluT1 is also an important target in cancer therapy as it is overexpressed in tumor cells. Previous studies have suggested that GluT1 mediates solute transfer through a cycle of conformational changes. However, the corresponding 3D structures adopted by the transporter during the transfer process remain elusive. In the present work, we first elucidate the whole conformational landscape of GluT1 in the absence of glucose, using long molecular dynamics simulations and show that the transitions can be accomplished through thermal fluctuations. Importantly, we highlight a strong coupling between intracellular and extracellular domains of the protein that contributes to the transmembrane helices reorientation during the transition. The conformations adopted during the simulations differ from the known 3D bacterial homologs structures resolved in similar states. In holo state simulations, we find that glucose transits along the pathway through significant rotational motions, while maintaining hydrogen bonds with the protein. These persistent motions affect side chains orientation, which impacts protein mechanics and allows glucose progression.

Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders

Purpose:

Untargeted metabolomic analysis is increasingly being used in the screening and management of individuals with inborn errors of metabolism (IEM). We aimed to test whether untargeted metabolomic analysis in plasma might be useful for monitoring the disease course and management of urea cycle disorders (UCDs).

Methods:

Untargeted mass spectrometry-based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various UCDs. Pathway analysis was used to identify common pathways that were perturbed in each UCD.

Results:

Our metabolomic analysis in plasma identified multiple potentially neurotoxic metabolites of arginine in arginase deficiency and, thus, may have utility in monitoring the efficacy of treatment in arginase deficiency. In addition, we were also able to detect multiple biochemical perturbations in all UCDs that likely reflect clinical management, including metabolite alterations secondary to dietary and medication management.

Conclusions:

In addition to utility in screening for IEM, our results suggest that untargeted metabolomic analysis in plasma may be beneficial for monitoring efficacy of clinical management and off-target effects of medications in UCDs and potentially other IEM.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.